# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import XLMConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( XLMForMultipleChoice, XLMForQuestionAnswering, XLMForQuestionAnsweringSimple, XLMForSequenceClassification, XLMForTokenClassification, XLMModel, XLMWithLMHeadModel, ) from transformers.models.xlm.modeling_xlm import create_sinusoidal_embeddings class XLMModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_lengths=True, use_token_type_ids=True, use_labels=True, gelu_activation=True, sinusoidal_embeddings=False, causal=False, asm=False, n_langs=2, vocab_size=99, n_special=0, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_sequence_label_size=2, initializer_range=0.02, num_labels=2, num_choices=4, summary_type="last", use_proj=True, scope=None, bos_token_id=0, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_lengths = use_input_lengths self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.gelu_activation = gelu_activation self.sinusoidal_embeddings = sinusoidal_embeddings self.causal = causal self.asm = asm self.n_langs = n_langs self.vocab_size = vocab_size self.n_special = n_special self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.summary_type = summary_type self.use_proj = use_proj self.scope = scope self.bos_token_id = bos_token_id def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = random_attention_mask([self.batch_size, self.seq_length]) input_lengths = None if self.use_input_lengths: input_lengths = ( ids_tensor([self.batch_size], vocab_size=2) + self.seq_length - 2 ) # small variation of seq_length token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.n_langs) sequence_labels = None token_labels = None is_impossible_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) is_impossible_labels = ids_tensor([self.batch_size], 2).float() choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() return ( config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ) def get_config(self): return XLMConfig( vocab_size=self.vocab_size, n_special=self.n_special, emb_dim=self.hidden_size, n_layers=self.num_hidden_layers, n_heads=self.num_attention_heads, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, gelu_activation=self.gelu_activation, sinusoidal_embeddings=self.sinusoidal_embeddings, asm=self.asm, causal=self.causal, n_langs=self.n_langs, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, summary_type=self.summary_type, use_proj=self.use_proj, num_labels=self.num_labels, bos_token_id=self.bos_token_id, ) def create_and_check_xlm_model( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): model = XLMModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, lengths=input_lengths, langs=token_type_ids) result = model(input_ids, langs=token_type_ids) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_xlm_lm_head( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): model = XLMWithLMHeadModel(config) model.to(torch_device) model.eval() result = model(input_ids, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_xlm_simple_qa( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): model = XLMForQuestionAnsweringSimple(config) model.to(torch_device) model.eval() outputs = model(input_ids) outputs = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels) result = outputs self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def create_and_check_xlm_qa( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): model = XLMForQuestionAnswering(config) model.to(torch_device) model.eval() result = model(input_ids) result_with_labels = model( input_ids, start_positions=sequence_labels, end_positions=sequence_labels, cls_index=sequence_labels, is_impossible=is_impossible_labels, p_mask=input_mask, ) result_with_labels = model( input_ids, start_positions=sequence_labels, end_positions=sequence_labels, cls_index=sequence_labels, is_impossible=is_impossible_labels, ) (total_loss,) = result_with_labels.to_tuple() result_with_labels = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels) (total_loss,) = result_with_labels.to_tuple() self.parent.assertEqual(result_with_labels.loss.shape, ()) self.parent.assertEqual(result.start_top_log_probs.shape, (self.batch_size, model.config.start_n_top)) self.parent.assertEqual(result.start_top_index.shape, (self.batch_size, model.config.start_n_top)) self.parent.assertEqual( result.end_top_log_probs.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top) ) self.parent.assertEqual( result.end_top_index.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top) ) self.parent.assertEqual(result.cls_logits.shape, (self.batch_size,)) def create_and_check_xlm_sequence_classif( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): model = XLMForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids) result = model(input_ids, labels=sequence_labels) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) def create_and_check_xlm_token_classif( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): config.num_labels = self.num_labels model = XLMForTokenClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_xlm_for_multiple_choice( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): config.num_choices = self.num_choices model = XLMForMultipleChoice(config=config) model.to(torch_device) model.eval() multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() result = model( multiple_choice_inputs_ids, attention_mask=multiple_choice_input_mask, token_type_ids=multiple_choice_token_type_ids, labels=choice_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths} return config, inputs_dict @require_torch class XLMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( XLMModel, XLMWithLMHeadModel, XLMForQuestionAnswering, XLMForSequenceClassification, XLMForQuestionAnsweringSimple, XLMForTokenClassification, XLMForMultipleChoice, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": XLMModel, "fill-mask": XLMWithLMHeadModel, "question-answering": XLMForQuestionAnsweringSimple, "text-classification": XLMForSequenceClassification, "text-generation": XLMWithLMHeadModel, "token-classification": XLMForTokenClassification, "zero-shot": XLMForSequenceClassification, } if is_torch_available() else {} ) # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_case_name, config_class, model_architecture, tokenizer_name, image_processor_name, feature_extractor_name, processor_name, ): if ( pipeline_test_case_name == "QAPipelineTests" and tokenizer_name is not None and not tokenizer_name.endswith("Fast") ): # `QAPipelineTests` fails for a few models when the slower tokenizer are used. # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework) # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer return True return False # XLM has 2 QA models -> need to manually set the correct labels for one of them here def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if model_class.__name__ == "XLMForQuestionAnswering": inputs_dict["start_positions"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) inputs_dict["end_positions"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) return inputs_dict def setUp(self): self.model_tester = XLMModelTester(self) self.config_tester = ConfigTester(self, config_class=XLMConfig, emb_dim=37) def test_config(self): self.config_tester.run_common_tests() def test_xlm_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_model(*config_and_inputs) # Copied from tests/models/distilbert/test_modeling_distilbert.py with Distilbert->XLM def test_xlm_model_with_sinusoidal_encodings(self): config = XLMConfig(sinusoidal_embeddings=True) model = XLMModel(config=config) sinusoidal_pos_embds = torch.empty((config.max_position_embeddings, config.emb_dim), dtype=torch.float32) create_sinusoidal_embeddings(config.max_position_embeddings, config.emb_dim, sinusoidal_pos_embds) self.model_tester.parent.assertTrue(torch.equal(model.position_embeddings.weight, sinusoidal_pos_embds)) def test_xlm_lm_head(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_lm_head(*config_and_inputs) def test_xlm_simple_qa(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_simple_qa(*config_and_inputs) def test_xlm_qa(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_qa(*config_and_inputs) def test_xlm_sequence_classif(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_sequence_classif(*config_and_inputs) def test_xlm_token_classif(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_token_classif(*config_and_inputs) def test_xlm_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_for_multiple_choice(*config_and_inputs) def _check_attentions_for_generate( self, batch_size, attentions, prompt_length, output_length, config, decoder_past_key_values ): # adds PAD dummy token, expected shape is off by 1 prompt_length += 1 output_length += 1 super()._check_attentions_for_generate( batch_size, attentions, prompt_length, output_length, config, decoder_past_key_values ) def _check_hidden_states_for_generate( self, batch_size, hidden_states, prompt_length, output_length, config, use_cache=False ): # adds PAD dummy token, expected shape is off by 1 prompt_length += 1 output_length += 1 super()._check_hidden_states_for_generate( batch_size, hidden_states, prompt_length, output_length, config, use_cache ) @slow def test_model_from_pretrained(self): model_name = "FacebookAI/xlm-mlm-en-2048" model = XLMModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_torch class XLMModelLanguageGenerationTest(unittest.TestCase): @slow def test_lm_generate_xlm_mlm_en_2048(self): model = XLMWithLMHeadModel.from_pretrained("FacebookAI/xlm-mlm-en-2048") model.to(torch_device) input_ids = torch.tensor([[14, 447]], dtype=torch.long, device=torch_device) # the president expected_output_ids = [ 14, 447, 14, 447, 14, 447, 14, 447, 14, 447, 14, 447, 14, 447, 14, 447, 14, 447, 14, 447, ] # the president the president the president the president the president the president the president the president the president the president # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference output_ids = model.generate(input_ids, do_sample=False) self.assertListEqual(output_ids[0].tolist(), expected_output_ids)